import cv2
import numpy as np


def load_images(front_image_path, side_image_path):
    # Load images
    front_img = cv2.imread(front_image_path, cv2.IMREAD_GRAYSCALE)
    side_img = cv2.imread(side_image_path, cv2.IMREAD_GRAYSCALE)
    return front_img, side_img


def find_keypoints_and_descriptors(image):
    # Initialize SIFT detector
    sift = cv2.SIFT_create()
    keypoints, descriptors = sift.detectAndCompute(image, None)
    return keypoints, descriptors


def match_features(desc1, desc2):
    # Initialize FLANN based matcher
    FLANN_INDEX_KDTREE = 1
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
    search_params = dict(checks=50)

    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(desc1, desc2, k=2)

    # Apply ratio test
    good_matches = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m)

    return good_matches


def estimate_depth(front_img, side_img, keypoints1, keypoints2, good_matches):
    # Assuming camera parameters are known (focal length, etc.)
    focal_length = 1.0  # Placeholder value
    known_distance = 10.0  # Known distance between the two images in real-world units

    # Calculate disparity
    disparities = []
    for match in good_matches:
        img1_idx = match.queryIdx
        img2_idx = match.trainIdx

        pt1 = keypoints1[img1_idx].pt
        pt2 = keypoints2[img2_idx].pt

        disparity = abs(pt1[0] - pt2[0])
        disparities.append(disparity)

    average_disparity = np.mean(disparities)

    # Calculate depth
    depth = (focal_length * known_distance) / average_disparity
    return depth


def main(front_image_path, side_image_path):
    front_img, side_img = load_images(front_image_path, side_image_path)

    keypoints1, descriptors1 = find_keypoints_and_descriptors(front_img)
    keypoints2, descriptors2 = find_keypoints_and_descriptors(side_img)

    good_matches = match_features(descriptors1, descriptors2)

    if len(good_matches) > 0:
        depth = estimate_depth(front_img, side_img, keypoints1, keypoints2, good_matches)
        print(f"Estimated Depth: {depth} units")
    else:
        print("No good matches found")


# Example usage
front_image_path = 'path_to_front_image.jpg'
side_image_path = 'path_to_side_image.jpg'
main(front_image_path, side_image_path)
